The SIMBAD project

The development of performance modelling methodologies able to translate new ATM concepts and technologies into their impact on high-level, system wide KPIs has been a long-time objective of the ATM research community. Bottom-up, microsimulation models are often the only feasible approach to address this problem in a reliable manner. However, the practical application of large-scale simulation models to strategic ATM performance assessment is often hindered by their computational complexity.

The SIMBAD project has developed an ATM Performance Modelling Framework aimed at enhancing the capabilities of large-scale ATM microsimulation models to effectively support performance evaluation at network level. The framework enables the comprehensive exploration and understanding of the performance impact of ATM concepts and solutions under different traffic conditions.


The goal of SIMBAD was to develop and evaluate a set of machine learning approaches aimed at providing state of-the-art ATM microsimulation models with the level of reliability, tractability and interpretability required to effectively support performance evaluation at ECAC level.

The specific objectives of the project were the following: 

  • Explore the use of machine learning techniques for the estimation of hidden variables from historical air traffic data, with particular focus on airspace users’ preferences and behaviour, in order to enable a more robust calibration of air traffic microsimulation models.
  • Develop new machine learning algorithms for the classification of traffic patterns that enable the selection of a sufficiently representative set of simulation scenarios allowing a comprehensive assessment of new ATM concepts and solutions.
  • Investigate the use of active learning metamodelling to facilitate a more efficient exploration of the input output space of complex simulation models through the development of more parsimonious performance metamodels, i.e., analytical input/output functions that approximate the results of a more complex function defined by the microsimulation models.
  • Demonstrate and evaluate the newly developed methods and tools through a set of case studies in which the proposed techniques will be integrated with existing, state-of-the-art ATM simulation tools and used to analyse a variety of ATM performance problems.

This project has received funding from the SESAR Joint Undertaking (JU) under grant agreement No 894241. The JU receives support from the European Union’s Horizon 2020 research and innovation programme and the SESAR JU members other than the Union. © 2020 AICHAIN Consortium.